{"id":"W3206499172","doi":"10.1007/s10955-021-02819-w","title":"Large Deviations for Subcritical Bootstrap Percolation on the Erdős–Rényi Graph","year":2021,"lang":"en","type":"article","venue":"Journal of Statistical Physics","topic":"Stochastic processes and statistical mechanics","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Central limit theorem; Combinatorics; Percolation (cognitive psychology); Random graph; Rate function; Distance; Graph; Percolation threshold; Large deviations theory; Limit (mathematics); Event (particle physics); Discrete mathematics; Statistical physics; Statistics; Physics; Mathematical analysis; Quantum mechanics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0004796259,0.0001579592,0.0003501655,0.00002833086,0.0002297414,0.00008166105,0.0001571412,0.00007297771,0.0001638736],"category_scores_gemma":[0.009604368,0.0001092454,0.0001669061,0.000204223,0.00006641919,0.00009427671,0.00002871904,0.0003728152,0.00001132576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005213368,"about_ca_system_score_gemma":0.0002053188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.780866e-7,"about_ca_topic_score_gemma":0.000002869512,"domain_scores_codex":[0.9983207,0.00008714415,0.0005914313,0.0001579505,0.0005280855,0.0003147087],"domain_scores_gemma":[0.9896241,0.008895481,0.0002033534,0.0001790419,0.0009396375,0.0001584155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000632891,0.0004244697,0.000003788392,0.00009931997,0.00006342376,0.00002851551,0.0001342248,0.000007877125,0.0001617098,0.9840041,0.01418179,0.0008274738],"study_design_scores_gemma":[0.0005750201,0.0002982065,0.0001021868,0.00008569506,0.0002118974,0.00003509512,0.0003547144,0.007246889,0.0003959379,0.9894617,0.001103954,0.000128676],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001252741,0.00004184819,0.9949486,0.002363221,0.0002790351,0.0001790233,0.0005550263,0.00001199999,0.0003684741],"genre_scores_gemma":[0.9109223,0.00003007979,0.08736309,0.0009272348,0.0005974205,0.00002034982,0.000024645,0.00003801329,0.00007688071],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9096696,"threshold_uncertainty_score":0.9987382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0977473687507872,"score_gpt":0.3797903567098131,"score_spread":0.2820429879590259,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}